Update app.py

app.py

@@ -1,26 +1,30 @@
 import gradio as gr
 from transformers import pipeline
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 
-def get_completion(inputs, parameters=None):
-    data = { "inputs": inputs }
-    if parameters is not None:
-        data.update({"parameters": parameters})
-    return pipeline(prompt, params)['sample'][0]
-    # response = requests.request("POST",
-    #                             ENDPOINT_URL,
-    #                             headers=headers,
-    #                             data=json.dumps(data))
-    # return json.loads(response.content.decode("utf-8"))
+# def get_completion(inputs, parameters=None):
+#     data = { "inputs": inputs }
+#     if parameters is not None:
+#         data.update({"parameters": parameters})
+#     return pipeline(prompt, params)['sample'][0]
+#     # response = requests.request("POST",
+#     #                             ENDPOINT_URL,
+#     #                             headers=headers,
+#     #                             data=json.dumps(data))
+#     # return json.loads(response.content.decode("utf-8"))
     
-# def get_completion(prompt,params):
-#     return pipeline(prompt=prompt, height=params['height'], width=params['width'], num_inference_steps=params['num_inference_steps'], guidance_scale=params['guidance_scale'], negative_prompt=params['negative_prompt'])['sample'][0]
-#     # return pipeline(prompt, params)['sample'][0]
+def get_completion(prompt,params):
+    return pipeline(prompt=prompt, height=params['height'], width=params['width'], 
+                    num_inference_steps=params['num_inference_steps'], 
+                    guidance_scale=params['guidance_scale'])['sample'][0]
+                    # negative_prompt=params['negative_prompt'])['sample'][0]
+
+    # return pipeline(prompt, params)['sample'][0]
 
 def generate(prompt,negative_prompt,steps,guidance,width,height):
     params = {
-        "negative_prompt": negative_prompt,
+        # "negative_prompt": negative_prompt,
         "num_inference_steps": steps,
         "guidance_scale": guidance,
         "width": width,
@@ -29,7 +33,7 @@ def generate(prompt,negative_prompt,steps,guidance,width,height):
     output = get_completion(prompt,params)
     return output
     
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
 
 with gr.Blocks() as demo:
     gr.Markdown("# Image Generation with Stable Diffusion")